H200 vs B300
A Generational
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The NVIDIA H200, built on the Hopper architecture, represented a significant step forward when it launched — doubling HBM capacity to 141GB of HBM3e while maintaining the proven GH200 Grace Hopper design. For many workloads, it remains the cost-effective choice.

The B300, part of the Blackwell Ultra family, rewrites the rulebook. With up to 288GB of HBM3e per GPU, a new Transformer Engine with native FP4 support, and NVLink 5 delivering 1.8TB/s of bisection bandwidth, it's designed for the multi-trillion parameter model era.

Memory capacity is where the B300 makes its most dramatic statement. At 288GB HBM3e per GPU, it more than doubles the H200's 141GB — enabling inference of 200B+ parameter models without tensor parallelism across multiple GPUs.

Bandwidth tells a similar story. The B300's HBM3e subsystem delivers 12 TB/s of aggregate memory bandwidth per node (8 GPUs), compared to roughly 4.8 TB/s for an equivalent H200 configuration. For memory-bound inference workloads, this is transformational.

The B300's Transformer Engine introduces native FP4 precision — a first for NVIDIA data center GPUs. This enables up to 2x the effective throughput on transformer-based inference compared to FP8, with minimal accuracy degradation for most production models.

On MLPerf v5.1 benchmarks, a single B300 node (8 GPUs) outperforms an H200 node by approximately 4x on large language model inference. Training throughput sees gains of roughly 2.5x, though the improvement varies significantly by model architecture and parallelization strategy.

NVLink 5, debuting with Blackwell Ultra, doubles the per-GPU interconnect bandwidth to 1.8 TB/s. This is critical for distributed training where gradient synchronization is the bottleneck. Multi-node NVLink domains (up to 576 GPUs connected via NVLink Switch) eliminate the PCIe/InfiniBand hop that plagued earlier architectures.

For the H200, NVLink 4 at 900 GB/s per GPU remains highly capable for clusters up to 256 GPUs. Beyond that scale, the interconnect overhead becomes measurable — particularly for models requiring all-reduce across more than 64 nodes.

Here's where it gets interesting for buyers. The B300's spot rate sits at approximately $5.10–$5.80/GPU/hr at current market prices, while H200s trade around $3.07–$3.16/GPU/hr. But raw hourly cost tells a misleading story.

When measured in cost-per-million-tokens for inference, the B300's FP4 capabilities and superior memory bandwidth deliver roughly 40–60% lower cost per token compared to H200 — depending on model size and batch configuration. For training, the gap narrows to approximately 20–30% savings on a per-FLOP basis.

The break-even analysis is straightforward: if your workload is inference-heavy (serving production models at scale), the B300 pays for itself almost immediately. If you're primarily training models under 70B parameters, the H200 remains the better value proposition at current spot rates.

For teams running production inference at scale — particularly serving models above 70B parameters — the B300 is the clear choice despite the higher per-hour cost. The total cost of ownership, measured in useful output per dollar, favors Blackwell Ultra significantly.

For research teams doing iterative training on models under 100B parameters, or teams running mixed training/inference workloads with moderate batch sizes, the H200 remains excellent value. At current spot rates through CheapestGPU, H200 clusters represent the best dollar-per-FLOP available in the market.

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